What do you do when the sky stops behaving the way the textbooks say it should?

For decades, talk of unidentified flying objects (“UFOs”) sat on the margins. That’s become harder to shrug off when decorated pilots, radar operators, and intel officers are raising their right hands in Congress and saying, I saw something I couldn’t file away as a drone, jet, or planet.

In this episode, we (JMill and Forrest) pull Unidentified Anomalous Phenomena (UAP) into familiar territory: sensors, safety, and systems. They start with what crews actually see and record. How does a night-vision video line up with radar tracks? What happens when the infrared feed disagrees with the eyeballs in the cockpit? And why does the boring part like timestamps, logs, and chain-of-custody matter even more now that anyone can synthesize a convincing “leak” in an afternoon?

Our conversation also zooms-out from individual cases to the wider mix. Many reports collapse to balloons, consumer drones, or reflected light once you have enough data. Some look a lot like foreign reconnaissance or test articles, which raises airspace and infrastructure questions. A small remainder still looks odd after serious review: abrupt accelerations, transmedium paths, signatures that do not match the usual catalog. So rather than chase headlines, the focus stays on a simpler test: what conditions would have to hold for this report to stand up?

We also talk about JMill's new MIT course, Confronting Unknowns, which treats UAP as one of several challenge areas for sense-making under pressure. Forrest connects us back to tough tech in general, in which fusion, lunar rovers, new materials (to name just a few areas) all begin as something messy, uncertain, and science fiction, before hard work and big discoveries could make them science fact.

So, the aim of the episode and the research we touch on is not to explain every light in the sky, but is instead a conversation on how we may practice how to think when the data are noisy, the stakes are serious, and “we don’t know yet” is an honest, useful starting point.

Tough tech is, almost by definition about building into uncertainty. UAPs are an extreme example, but the mindset – rigor, curiosity, and a willingness to update one's assumptions – is wholly shared.

📺Watch:

https://youtu.be/v4uqp6SXC3g

🧠Relevant Links:

• Episode homepage
• Forrest Meyen on LinkedIn
• JMill on LinkedIn
• MIT Confronting Unknowns course on Aerospace Safety and Anomalous Phenomena
• Errata: The MIT course about operating aircraft is 16.767: Introduction to Airline Transport Aircraft Systems and Automation

👏Credit Roll:

• Producers: Jonathan 'JMill' Miller and Forrest Meyen
• Hosts: JMill and Forrest
• Editing: Forrest
• Transcript: JMill with AI assistance
• Blog Author: JMill
• Art Direction: JMill

🔖Topic Timecodes:
00:00:02 Cozy open with fireside vibe
00:01:40 What UAP means here
00:03:45 Observables that defy expectations
00:06:30 The Hellfire clip and skepticism
00:07:56 Why chain of custody matters
00:10:53 Transmedium claims and the physics check
00:14:09 Citizen science toolkit: ADS-B, stars, satellites
00:17:20 Hypotheses from mundane to exotic
00:21:22 Adversary tech or unknowns
00:25:40 Balloons, filter retuning, detection gaps
00:29:47 Sensors, electronic warfare, and analog film
00:33:16 Course preview: MIT Confronting Unknowns
00:44:57 Lunar Outpost mission validation

The way to improve physical intelligence is to simulate, discover, and do.

A dropped box tells the truth. That little skid and thud is a progress report one can feel. In this episode, Tough Tech Today Co-Hosts Forrest and JMill trace the full arc of “physical” intelligence: how we simulate the world, discover what to build, and then make hardware that learns while doing real work.

First, simulate: Before a wheel ever touches regolith or a gripper meets a crate, we spin up physics-rich worlds and run them by the thousands. This is not to find a perfect script, but to survive the imperfect ones. If a machine’s behavior holds up to domain randomization, messy lighting, uncertain friction… it may stand a chance on day one.

Then, discover: Imagine an autonomous lab bench where pipettes, sensors, and models conspire to explore their search space. The point is not cute demos, but rather new catalysts, sturdier materials, better routes to medicines. Humans keep the compass while the system earns its stripes by proposing and testing the next steps.

Finally, do: The shop floor is where timing and torque decide what actually works. Machine vision has been around for decades; what changes now is adaptation. Tactile data, trustworthy actuation, and feedback loops tight enough to correct mid-cycle help make open loops into closed loops. Ideally, we see waste reduce, uptime climb, and skills accumulate.

We also discuss the future of work and work-of-the-future. General-purpose agents capabilities will probably not arrive in a headline, instead percolating as a thousand small skills that survive contact with clutter, dust, heat, and schedule slips. That means building for failure an organization can recover from, staging rollouts, red-teaming the edge cases, and being clear who is on the hook when something goes wrong.

If we get this right, the wins will look, well, ordinary: fewer knobs to tune, fewer reworks, more jobs finished on time. But what is exception is that models are becoming matter, the foundation of systems that quietly improve with use.

And what about that dropped box? It becomes a better grasp the very next cycle, with the machine’s learnings shared across a hundred robots operating among a swarm, broadcast to thousands of work cells around the world.

P.S. Thank you to our tough tech champions. We really appreciate your support. We have pay-if-you-can membership options so you can help us bring Tough Tech Today to more folks!

📺Watch:

https://youtu.be/UqRX5h1aUF8

🧠Relevant Links:

• Episode homepage
• Forrest Meyen on LinkedIn
• JMill on LinkedIn
• MIT News: Using generative AI to help robots jump higher and land safely

👏Credit Roll:

• Producers: Jonathan 'JMill' Miller and Forrest Meyen
• Hosts: JMill and Forrest
• Editing: JMill
• Transcript: JMill with AI assistance
• Blog Author: JMill
• Art Direction: JMill

🔖Topic Timecodes:

00:00:00 Welcome Back: Machines and Intelligence
00:01:05 Defining “Physical AI”
00:03:10 Robots for Extreme Environments
00:06:00 Sim to Real: Digital Twins
00:09:20 Humanoids and the Future of Work
00:12:15 Manufacturing 2.0: AI for Materials
00:16:00 Ground Truth vs Synthetic Data
00:19:40 Space Ops: Millions of Sim Cycles
00:23:05 Terrain Truthing: Sand and Regolith
00:26:50 Agents That Tune the Models
00:30:30 Generative CAD, Patents, Liability
00:33:45 Guardrails, Audit

Under New York City lies a patchwork of long-dormant dark fibers that are now illuminating with entangled rubidium atoms.

In our conversation with Noel Goddard, CEO of Qunnect, we learn about the tough tech testbed named GothamQ. Qunnect is pioneering the future of the telecom industry by transforming existing fiber optics into quantum networks, providing a robust, secure communication channel for the next evolution of the digital age.

Qunnect’s rubidium-based networking devices promise to safeguard communications against eavesdropping by ensuring that any attempts at interception destroy the transmitted message. This makes digital communications that are provably secure, and is gaining attention as a key component of a layered approach to quantum networking solutions that meet the needs of clients ranging from enterprise players to government agencies.

“We work in atomic vapor,” Goddard says, “we use the same vapor that they [atomic clocks for Global Positioning Systems] do. What's nice about it is that atoms are the same everywhere in the universe, so whether it's outer space, underwater, on a mountain… atoms provide a very high precision reference tool.” Quantum networking protocols require sophisticated levels of precision “which has never been seen by normal digital communications”, adding that “atoms offer a very interesting way to do that because of this ability to locally reference them” at scale. Qunnect’s networking hardware is a physical manifestation of decades of basic and applied research into quantum physics, information theory, and optics, with the GothamQ test site as the next evolution in the broadening deployment of their hardware portfolio composed of quantum sources, quantum memory, wavelength referencers, and more.

In a candid moment, Goddard acknowledges the unique challenges facing quantum science startups, including the need for substantial capital investment and the patience to develop highly complex and sensitive technologies. However, she remains optimistic about the future, highlighting the growing interest and support from government and corporate investors willing to take long-term bets on tough tech innovations.

Goddard reflects on the broader implications of quantum security. By providing a fundamentally secure communication method, Qunnect isn’t just advancing technology but also safeguarding economic and national security in an increasingly digital world.

P.S. If you're in the Boston-Cambridge area this fall, check out JMill's Tough Tech On Tap, presented by The End Effector!

P.P.S. Thank you to our tough tech champions. We really appreciate your support. We have pay-if-you-can membership options so you can help us bring Tough Tech Today to more folks!

📺Watch on YouTube

🧠Relevant Links:

• Episode homepage
• Noel Goddard on LinkedIn
• Qunnect homepage

👏Credit Roll:

• Producers: Jonathan 'JMill' Miller and Forrest Meyen
• Guest: Noel Goddard
• Host: JMill
• Editing: JMill
• Transcript: JMill
• Blog Author: JMill
• Art Design: JMill